Forensic Face Recognition: A Survey

Beside a few papers which focus on the forensic aspects of automatic face recognition, there is not much published about it in contrast to the literature on developing new techniques and methodologies for biometric face recognition. In this report, we review forensic facial identification which is the forensic experts‟ way of manual facial comparison. Then we review famous works in the domain of forensic face recognition. Some of these papers describe general trends in forensics [1], guidelines for manual forensic facial comparison and training of face examiners who will be required to verify the outcome of automatic forensic face recognition system [2]. Some proposes theoretical framework for application of face recognition technology in forensics [3] and automatic forensic facial comparison [4, 5]. Bayesian framework is discussed in detail and it is elaborated how it can be adapted to forensic face recognition. Several issues related with court admissibility and reliability of system are also discussed. \ud Until now, there is no operational system available which automatically compare image of a suspect with mugshot database and provide result usable in court. The fact that biometric face recognition can in most cases be used for forensic purpose is true but the issues related to integration of technology with legal system of court still remain to be solved. There is a great need for research which is multi-disciplinary in nature and which will integrate the face recognition technology with existing legal systems. In this report we present a review of the existing literature in this domain and discuss various aspects and requirements for forensic face recognition systems particularly focusing on Bayesian framework

A review of calibration methods for biometric systems in forensic applications

When, in a criminal case there are traces from a crime scene - e.g., finger marks or facial recordings from a surveillance camera - as well as a suspect, the judge has to accept either the hypothesis \emph{$H_{p}$} of the prosecution, stating that the trace originates from the subject, or the hypothesis of the defense \emph{$H_d$}, stating the opposite. The current practice is that forensic experts provide a degree of support for either of the two hypotheses, based on their examinations of the trace and reference data - e.g., fingerprints or photos - taken from the suspect. There is a growing interest in a more objective quantitative support for these hypotheses based on the output of biometric systems instead of manual comparison. However, the output of a score-based biometric system is not directly suitable for quantifying the evidential value contained in a trace. A suitable measure that is gradually becoming accepted in the forensic community is the Likelihood Ratio (LR) which is the ratio of the probability of evidence given \emph{$H_p$} and the probability of evidence given \emph{$H_d$}. In this paper we study and compare different score-to-LR conversion methods (called calibration methods). We include four methods in this comparative study: Kernel Density Estimation (KDE), Logistic Regression (Log Reg), Histogram Binning (HB), and Pool Adjacent Violators (PAV). Useful statistics such as mean and bias of the bootstrap distribution of \emph{LRs} for a single score value are calculated for each method varying population sizes and score location

Understanding Evil: Reflections on Thought Action and Punishment

Actions are the basis for moral judgment. In this paper, I develop a concept of action that illustrates the differences between bad, evil, and sadistic actions. Using this theory of action, I apply it to punishment theory and the philosophy of criminal law. Bad and evil actions are defined by differences in their magnitude, as measured by the ability of the victim to recover from harm. I propose that sadistic actions comprise a qualitatively unique form of wrongdoing. They are performed following a bad or evil action with the intent to add insult to injury. I propose that within a retributivist framework, this theory of action allows for a more effective approach to punishing bad and evil actions. And within a consequentialist framework, this theory of action allows for a more effective approach to punishing sadistic actions

UNEXPECTED QUARTERLY EARNINGS ANNOUNCEMENTS, FIRM SIZE, AND STOCK PRICE REACTION

This study exami nes the stock price react ion to theunexpected quarterly earnings announcements made by listed firmsin Pakistan using the data of 433 announcements made by 264 firmsfor a 2-year period from 2010 to 2011.Empirical results show that theunexpected quarterly earnings announcements do not have anysignificant price reaction in the Pakistani market. Positive andnegative abnormal returns are observed for firms making favorableand unfavorable earning announcements, respectively; but thesereturns are not statistically significant. Moreover, the study finds thatt he pri c e re act i on t o t he une x pe c t e d quart e rl y e arni ngsannouncement is not related to the size of the fir